Ultrasound-based diagnostic imaging has achieved widespread acceptance in clinical use. For example, modern ultrasound imaging diagnostic systems and techniques are commonly used to produce two-dimensional (2-D) or three-dimensional (3-D) ultrasound images of internal features of patients. Conventional ultrasound systems operate in various image modes (e.g., brightness mode or B-Mode, Doppler mode, etc.) to acquire ultrasound images for diagnoses. As a further example, traditional ultrasound systems can provide a color flow image, e.g., in a Doppler mode, that can represent velocities of moving objects such as blood flow, heart motions, etc. Such systems can be limited in that they measure motion parallel to the beam direction. Motion that is perpendicular to the beam direction typically cannot be measured. Moreover, in many instances, it is desirable to obtain angle-independent measurements that make use of the unique interference patterns called speckles, which are formed when ultrasound waves are reflected from underlying tissue scatterers and interfere with each other.
Accurate tissue motion estimation is beneficial in many diagnostic ultrasound applications. For instance, accurate 3-D tissue deformation analysis, or tissue motion estimation, finds many applications in ultrasound-based diagnostic imaging. As an example, accurate tissue motion estimation is especially helpful in the fields of elastography (e.g., for tumor detection) and echocardiography (e.g., for heart disease diagnosis). Therefore, accurate tissue deformation analysis via speckle tracking has great potential in clinical applications.
Speckle tracking assumes that the speckles in images remain unchanged before and after tissue motion. However, this assumption is true only for certain types of tissue motion (such as translation). In practice, speckles do change after tissue deformation, resulting in a difficult feature-motion decorrelation problem. Thus, systems employing typical speckle tracking-based estimation results do not use such results to represent the underlying true tissue motion.
To achieve accurate tissue motion estimation, compensation of feature-motion decorrelation can be performed to represent underlying true tissue motion. However, feature-motion decorrelation remains a largely open problem for ultrasound image-based tissue deformation analysis. Compensation of feature-motion decorrelation has been shown to be an ill-posed inverse problem. As accurate tissue motion estimation is beneficial in many diagnostic applications, feature-motion decorrelation can pose a challenge to implementing accurate tissue deformation analysis for ultrasound imaging clinical applications.
To alleviate the feature-motion decorrelation problem, typical solutions use relatively high frame rates (e.g., 200 frames per second in 2-D ultrasound imaging) during imaging so that the deformation between two neighboring ultrasound images is small enough to guard against any significant image variation. However, such high frame rates have been difficult to achieve for 3-D ultrasound imaging. In addition, while such high frame rates are possible in 2-D ultrasound imaging, out-of-plane motion (e.g., the tissue part may not always stay in the same 2-D imaging plane as assumed) hampers the wide applications of 2-D ultrasound imaging in tissue deformation analysis. Also, the problems of feature-motion decorrelation (e.g., the problem of accurate tissue deformation analysis) remain unaddressed by high frame rate solutions.
Conventional image-analysis approaches to the problem of feature-motion decorrelation either place additional constraints to limit the search space during motion tracking, or model image variations caused by tissue motion. However, regardless of the approach posed by conventional solutions, neither adequately addresses the problems of accurate tissue deformation analysis (e.g., feature-motion decorrelation).
For example, while constraint-based image-analysis approaches (e.g., tissue incompressibility models, deformable mesh method, finite-element method, multi-scale estimation with regularized displacement fields, etc.) address the larger problem by focusing on the end result, such approaches constrain the solution space rather than resolving the issue of feature-motion decorrelation.
As a further example, modeling image variations caused by tissue motion (e.g., 2-D companding) can model 2-D image variations caused by tissue motion with a 2-D scaling plus a shift. 2-D companding uses a multi-scale framework to estimate the scaling and shift parameters. For instance, after warping an image taken after tissue deformation, 2-D companding can estimate tissue displacement and can further derive tissue strain distribution. However, while companding can provide an acceptable approximation of image variation for a limited range of tissue deformation, typical systems employing companding do not provide acceptable results for large tissue deformation.
It is thus desired to provide enhanced systems, devices, and methodologies for compensation of feature-motion decorrelation to facilitate tissue deformation analysis in ultrasound imaging systems that improve upon these and other deficiencies. The above-described deficiencies of typical ultrasound imaging systems are merely intended to provide an overview of some of the problems of conventional systems, and are not intended to be exhaustive. Other problems with conventional systems and corresponding benefits of the various non-limiting embodiments described herein may become further apparent upon review of the following description.